Machine-Learning Compression for Particle Physics Discoveries

Understand

In collider-based particle and nuclear physics experiments, data are produced at such extreme rates that only a subset can be recorded for later analysis.

• Typically, algorithms select individual collision events for preservation and store the complete experimental response.
• A relatively new alternative strategy is to additionally save a partial record for a larger subset of events, allowing for later specific analysis of a larger fraction of events.
• We propose a strategy that bridges these paradigms by compressing entire events for generic offline analysis but at a lower fidelity.